Building a Zero-Hallucination MCP Rule-Verdict Agent in 6 Hours Without Writing Code

In rapid software development and hackathons, technical bottlenecks usually manifest as boilerplate writing, syntax debugging, or managing unpredictable LLM non-determinism. When building AERO-KIT—a deterministic Model Context Protocol (MCP) agent designed to solve complex industrial and legal rule contradictions—the paradigm shifted entirely. Not a single line of application code was hand-written.

Instead, the workflow relied on system architecture and prompt-driven orchestration. The human acted as Chief Architect, while a coordinated AI engine stack handled file generation, test bench synthesis, and system configurations. By leveraging model aggregation platforms like n1n.ai to route workloads to optimal models, high-performance execution was achieved without manual code drafting.

This article breaks down the architectural principles, knowledge modeling strategies, and protocol-driven guardrails required to build a zero-hallucination rule arbitration agent in under six hours.

The Fundamental Flaw of Vector RAG in Rule Arbitration

Most retrieval-based AI agents fail when resolving explicit rule conflicts because they rely primarily on vector similarity search (Vector RAG).

Consider a standard compliance scenario:

• Base Policy Rule (ID: R-101): "Maximum allowable operating temperature for Component A is 75°C."
• Errata Amendment (ID: E-502, Precedence Level 3): "Under high-pressure conditions, maximum allowable operating temperature for Component A is reduced to 60°C, overriding Rule R-101."

Naive Vector Search (Similarity Match)
  │
  ├── Rule R-101 (75°C) ── Cosine Similarity: 0.89 ──┐
  │                                                   ├──> Conflict! LLM averages or guesses (e.g., 67.5°C or picks R-101)
  └── Errata E-502 (60°C) ── Cosine Similarity: 0.88 ──┘

When an engineer queries the agent about component safety under high pressure, vector search converts both sentences into embedding vectors. Because both passages share identical semantic contexts (temperature limits for Component A), cosine similarity scores are virtually identical. The LLM then receives both conflicting fragments in its context window without explicit relational logic, leading to:

1. Hallucination by averaging: Guessing an intermediate value (e.g., 67.5°C).
2. Authority inversion: Preferring the older base policy because it matches the query keywords more closely.
3. Unpredictable output: Varying answers across different query iterations.

AERO-KIT bypasses vector similarity entirely by structuring knowledge as an explicit relational graph hosted on Sanity Content Lake and querying it through Anthropic's Model Context Protocol (MCP).

System Architecture: The Zero-Code Multi-Agent Stack

To build a high-precision agent without manual coding, responsibilities must be strictly separated across three layers: System Direction, Execution Engine, and Structured Knowledge Lake.

+-----------------------------------------------------------------------+
|                         CHIEF ARCHITECT (Human)                       |
|               Provides Intent, Guardrails & Declarative Specs         |
+-----------------------------------------------------------------------+
                                   │
                                   ▼
+-----------------------------------------------------------------------+
|                    STRATEGIC BRAIN & ORCHESTRATION                    |
|           Models like Gemini 1.5 Pro / Claude 3.5 via n1n.ai          |
|      - Formulates Data Schemas                                        |
|      - Generates GROQ Traversal Logic                                 |
|      - Enforces Strict Output Guardrails                              |
+-----------------------------------------------------------------------+
                                   │
                                   ▼
+-----------------------------------------------------------------------+
|                       LOCAL EXECUTION ENGINE                          |
|                       OpenCode (Big Pickle)                           |
|      - Emits Configs, Setup Protocols, & Integration Code             |
|      - Executes Node.js Automated Test Suites (node --test)           |
+-----------------------------------------------------------------------+
                                   │
                                   ▼
+-----------------------------------------------------------------------+
|                     STRUCTURED KNOWLEDGE GRAPH                        |
|                       Sanity Content Lake                             |
|      - Strict Linked Schemas: policyOrRule, errataOrAmendment        |
|      - Graph Queries via GROQ & Deterministic MCP Endpoint            |
+-----------------------------------------------------------------------+

Accessing diverse models through platforms like n1n.ai ensures that specialized tasks—such as logical schema generation or fast code emission—are assigned to the most suited LLM architecture without integration overhead.

Step 1: Knowledge Modeling in Sanity Content Lake

Rather than ingesting unstructured PDF documentation into a vector database, documents are chunked into typed, strongly linked entities inside Sanity.

Schema Structure

Three main document types form the schema:

1. policyOrRule: Holds the baseline rule text, scope, and initial parameters.
2. errataOrAmendment: Contains explicit references to target rules (targetRuleRef), condition modifiers, and an integer-based precedenceLevel.
3. resolutionCase: Stores human-validated edge cases for historic audit trails.

Below is the declarative schema definition generated directly by the strategic AI prompt:

// Schema Definition: errataOrAmendment.js
export default {
  name: 'errataOrAmendment',
  title: 'Errata or Amendment',
  type: 'document',
  fields: [
    { name: 'title', type: 'string', title: 'Title' },
    { name: 'amendmentCode', type: 'string', title: 'Amendment Code' },
    { 
      name: 'targetRuleRef', 
      type: 'reference', 
      to: [{ type: 'policyOrRule' }],
      title: 'Target Base Rule' 
    },
    { 
      name: 'precedenceLevel', 
      type: 'number', 
      title: 'Precedence Level (Higher overrides lower)' 
    },
    { name: 'conditionContext', type: 'string', title: 'Condition Context' },
    { name: 'updatedValue', type: 'string', title: 'Updated Normative Value' }
  ]
}

Step 2: Deterministic MCP Traversal with GROQ

Model Context Protocol (MCP) enables LLMs to invoke stateful tools securely. Instead of allowing the model to perform arbitrary searches, AERO-KIT exposes dedicated MCP tools that execute deterministic GROQ (Graph-Relational Object Queries) against the Sanity Content Lake.

When a user asks: "What is the maximum operating temperature for Component A under high pressure?" the agent calls an MCP tool running a specialized GROQ query:

*[ _type == "policyOrRule" && component == "Component A" ] {
  _id,
  ruleCode,
  baseValue,
  "amendments": *[ 
    _type == "errataOrAmendment" && 
    references(^._id) && 
    conditionContext match "high-pressure"
  ] | order(precedenceLevel desc) {
    amendmentCode,
    precedenceLevel,
    updatedValue,
    conditionContext
  }
}

Resolution Logic

The GROQ query engine traverses the graph:

1. It locates base rule R-101.
2. It fetches all linked errataOrAmendment nodes pointing to R-101.
3. It filters by context (high-pressure) and sorts by precedenceLevel in descending order.
4. It returns a structured JSON payload containing both the base value and the active override.

Because the data resolution happens inside the graph database execution engine rather than the LLM context, context mixing is impossible.

Step 3: Anti-Hallucination Guardrails & "Skills" Methodology

To guarantee zero hallucinations, system prompts must act as rigid runtime contracts. Borrowing from internal software engineering disciplines (such as testComplet and fixe-de-bug methodologies), the agent is bound by strict execution policies:

### SYSTEM EXECUTION POLICY: Deterministic Rule Arbitration

1. NO GUESSING POLICY:
   If a rule inquiry yields ambiguous data and no higher-precedence `errataOrAmendment` node exists to explicitly resolve the conflict, you MUST state: "UNRESOLVED_CONTRADICTION" and output the conflicting document IDs. Never attempt to synthesize an intermediate numeric value.

2. MANDATORY CITATION POLICY:
   Every parameter in your response MUST explicitly cite the exact `_id` and `precedenceLevel` returned by the `groq_query` MCP tool.

3. OUT-OF-BOUNDS BOUNDARY:
   If the query references entities absent from the Sanity Content Lake, reject the request immediately. Do not fallback to pre-trained internal knowledge.

Comparison: Vector RAG vs. Deterministic MCP Graph

Building resilient agent pipelines requires dependable model gateways. Leveraging services like n1n.ai allows enterprise architectures to dynamically route fallback traffic across Claude, OpenAI, and Gemini endpoints, maintaining high availability for critical MCP tool calls.

Step 4: Verification via Automated Node.js Test Suite

To validate the architecture, the local nano-engine (OpenCode) generated an automated integration test suite (node --test) to run continuous assertions against the MCP tool outputs.

import test from 'node:test';
import assert from 'node:assert/strict';
import \{ executeMcpQuery \} from './mcpClient.js';

test('TEST 2: Explicit Conflict Resolution via Precedence', async (t) => \{
  const result = await executeMcpQuery(\{
    query: "What is the max temp for Component A in high-pressure?